Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": false,
+  "pooling_mode_mean_tokens": true,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

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+---
+base_model: microsoft/mpnet-base
+datasets:
+- mertcobanov/all-nli-triplets-turkish
+language:
+- en
+- tr
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:120781
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Bir köpek sahibi, evcil hayvanıyla birlikte koşuyor ve evcil hayvan
+    bir parkurda engellerden kaçınıyor.
+  sentences:
+  - Bazı bitkilerin önünde mavi bir kano.
+  - Bir adam köpeğinin yanında koşuyor.
+  - Adam bir kediyle birlikte.
+- source_sentence: Parlamenter bölümünün patronunun ev hizmetiyle bağlantılı bir politikacı,
+    0-609-3459812 numaralı cep telefonuna sahip ve mizah anlayışının olmamasıyla tanınıyor,
+    'Hayran' adlı birinden gelen 'En iyi kürek dilekleri' mesajını pek iyi karşılamadı.
+  sentences:
+  - Doktor Perennial, kötü niyetli çavuş uyandığında ayakta duruyordu.
+  - Politikacı, patronunun ev hizmetini aradığında, bir 'hayran'dan gelen bir mesaja
+    pek hoş karşılamadı.
+  - Mesajı aldığı için o kadar minnettardı ki, gönderen kişiye bir demet çiçek gönderdi.
+- source_sentence: Bankanın kasalarında.
+  sentences:
+  - Ayakta duran bir insan
+  - Banka kasasında.
+  - Bankadaki kasa.
+- source_sentence: Bir grup Asyalı erkek, birlikte bir yemek yedikten sonra büyük
+    bir masanın etrafında poz veriyor.
+  sentences:
+  - Bir grup Asyalı erkek birlikte bir yemek yedi.
+  - Pazarlar, kaplıcalar ve kayak pistleri burada bulunan diğer cazibe merkezlerinden
+    bazılarını oluşturuyor.
+  - Bir grup Asyalı erkek futbol oynuyor.
+- source_sentence: Böyle şeyler görmek ve eğer yapabileceğiniz en küçük bir şey varsa,
+    bu yardımcı olur.
+  sentences:
+  - Böyle bir şeyi gözlemlemek ve yapıp yapamayacağınızı bilmek için.
+  - Adamın gömleği, kot pantolonundan farklı bir renkte.
+  - Böyle bir şeyi görmek kötü, eğer yapabiliyorsanız buna hiç katkıda bulunmayın.
+model-index:
+- name: SentenceTransformer based on microsoft/mpnet-base
+  results:
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: all nli dev turkish
+      type: all-nli-dev-turkish
+    metrics:
+    - type: cosine_accuracy
+      value: 0.7764277035236938
+      name: Cosine Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: all nli test turkish
+      type: all-nli-test-turkish
+    metrics:
+    - type: cosine_accuracy
+      value: 0.7740959297927069
+      name: Cosine Accuracy
+---
+
+# SentenceTransformer based on microsoft/mpnet-base
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [all-nli-triplets-turkish](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [all-nli-triplets-turkish](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish)
+- **Languages:** en, tr
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
+- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+
+### Full Model Architecture
+
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("mertcobanov/mpnet-base-all-nli-triplet-turkish-v4-dgx")
+# Run inference
+sentences = [
+    'Böyle şeyler görmek ve eğer yapabileceğiniz en küçük bir şey varsa, bu yardımcı olur.',
+    'Böyle bir şeyi gözlemlemek ve yapıp yapamayacağınızı bilmek için.',
+    'Böyle bir şeyi görmek kötü, eğer yapabiliyorsanız buna hiç katkıda bulunmayın.',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Triplet
+
+* Datasets: `all-nli-dev-turkish` and `all-nli-test-turkish`
+* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
+
+| Metric              | all-nli-dev-turkish | all-nli-test-turkish |
+|:--------------------|:--------------------|:---------------------|
+| **cosine_accuracy** | **0.7764**          | **0.7741**           |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### all-nli-triplets-turkish
+
+* Dataset: [all-nli-triplets-turkish](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish) at [13554fd](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish/tree/13554fdb2675c44f84a8dccc1afb51cee8a1e4ba)
+* Size: 120,781 training samples
+* Columns: <code>anchor_translated</code>, <code>positive_translated</code>, and <code>negative_translated</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor_translated                                                                 | positive_translated                                                              | negative_translated                                                               |
+  |:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                           | string                                                                            |
+  | details | <ul><li>min: 3 tokens</li><li>mean: 11.77 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 11.1 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 12.41 tokens</li><li>max: 44 tokens</li></ul> |
+* Samples:
+  | anchor_translated                                                                                                    | positive_translated                                                             | negative_translated                                                         |
+  |:---------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|:----------------------------------------------------------------------------|
+  | <code>Bir kişi, bir atın üzerinde, bozulmuş bir uçağın üzerinden atlıyor.</code>                                     | <code>Bir kişi dışarıda, bir atın üzerinde.</code>                              | <code>Bir kişi bir lokantada omlet siparişi veriyor.</code>                 |
+  | <code>Bir Küçük Lig takımı, bir oyuncunun bir üsse kayarak girmeye çalıştığı sırada onu yakalamaya çalışıyor.</code> | <code>Bir takım bir koşucuyu dışarı atmaya çalışıyor.</code>                    | <code>Bir takım Satürn'de beyzbol oynuyor.</code>                           |
+  | <code>Kadın beyaz giyiyor.</code>                                                                                    | <code>Beyaz bir ceket giymiş bir kadın bir tekerlekli sandalyeyi itiyor.</code> | <code>Siyah giyinmiş bir adam, siyah giyinmiş bir kadını kucaklıyor.</code> |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Evaluation Dataset
+
+#### all-nli-triplets-turkish
+
+* Dataset: [all-nli-triplets-turkish](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish) at [13554fd](https://huggingface.co/datasets/mertcobanov/all-nli-triplets-turkish/tree/13554fdb2675c44f84a8dccc1afb51cee8a1e4ba)
+* Size: 6,584 evaluation samples
+* Columns: <code>anchor_translated</code>, <code>positive_translated</code>, and <code>negative_translated</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | anchor_translated                                                                 | positive_translated                                                               | negative_translated                                                               |
+  |:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
+  | type    | string                                                                            | string                                                                            | string                                                                            |
+  | details | <ul><li>min: 2 tokens</li><li>mean: 22.3 tokens</li><li>max: 135 tokens</li></ul> | <ul><li>min: 1 tokens</li><li>mean: 10.92 tokens</li><li>max: 41 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 10.81 tokens</li><li>max: 34 tokens</li></ul> |
+* Samples:
+  | anchor_translated                                                                                                                           | positive_translated                                                                              | negative_translated                                                                                                  |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------|
+  | <code>Ayrıca, bu özel tüketim vergileri, diğer vergiler gibi, hükümetin ödeme zorunluluğunu sağlama yetkisini kullanarak belirlenir.</code> | <code>Hükümetin ödeme zorlaması, özel tüketim vergilerinin nasıl hesaplandığını belirler.</code> | <code>Özel tüketim vergileri genel kuralın bir istisnasıdır ve aslında GSYİH payına dayalı olarak belirlenir.</code> |
+  | <code>Gri bir sweatshirt giymiş bir sanatçı, canlı renklerde bir kasaba tablosu üzerinde çalışıyor.</code>                                  | <code>Bir ressam gri giysiler içinde bir kasabanın resmini yapıyor.</code>                       | <code>Bir kişi bir beyzbol sopası tutuyor ve gelen bir atış için planda bekliyor.</code>                             |
+  | <code>İmkansız.</code>                                                                                                                      | <code>Yapılamaz.</code>                                                                          | <code>Tamamen mümkün.</code>                                                                                         |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `learning_rate`: 2e-05
+- `num_train_epochs`: 10
+- `warmup_ratio`: 0.1
+- `fp16`: True
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 2e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: True
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | Validation Loss | all-nli-dev-turkish_cosine_accuracy | all-nli-test-turkish_cosine_accuracy |
+|:------:|:----:|:-------------:|:---------------:|:-----------------------------------:|:------------------------------------:|
+| 0      | 0    | -             | -               | 0.5729                              | -                                    |
+| 0.2119 | 100  | 6.6103        | 4.5154          | 0.6970                              | -                                    |
+| 0.4237 | 200  | 5.1602        | 3.7328          | 0.7195                              | -                                    |
+| 0.6356 | 300  | 4.4533        | 3.3389          | 0.7372                              | -                                    |
+| 0.8475 | 400  | 3.4465        | 3.6044          | 0.7187                              | -                                    |
+| 1.0572 | 500  | 2.6977        | 3.3043          | 0.7418                              | -                                    |
+| 1.2691 | 600  | 3.8142        | 3.2066          | 0.7512                              | -                                    |
+| 1.4809 | 700  | 3.4333        | 3.0716          | 0.7508                              | -                                    |
+| 1.6928 | 800  | 3.1488        | 2.9590          | 0.7553                              | -                                    |
+| 1.9047 | 900  | 1.8677        | 3.2416          | 0.7442                              | -                                    |
+| 2.1144 | 1000 | 2.2034        | 2.9323          | 0.7634                              | -                                    |
+| 2.3263 | 1100 | 2.9834        | 2.9406          | 0.7669                              | -                                    |
+| 2.5381 | 1200 | 2.6785        | 2.8607          | 0.7672                              | -                                    |
+| 2.75   | 1300 | 2.5096        | 2.8939          | 0.7684                              | -                                    |
+| 2.9619 | 1400 | 0.876         | 3.2539          | 0.7416                              | -                                    |
+| 3.1716 | 1500 | 2.3355        | 2.7503          | 0.7758                              | -                                    |
+| 3.3835 | 1600 | 2.4666        | 2.7920          | 0.7707                              | -                                    |
+| 3.5953 | 1700 | 2.2691        | 2.7860          | 0.7729                              | -                                    |
+| 3.8072 | 1800 | 1.8024        | 2.9899          | 0.7571                              | -                                    |
+| 4.0169 | 1900 | 0.6443        | 3.0993          | 0.7456                              | -                                    |
+| 4.2288 | 2000 | 2.3976        | 2.7792          | 0.7811                              | -                                    |
+| 4.4407 | 2100 | 2.1145        | 2.7968          | 0.7728                              | -                                    |
+| 4.6525 | 2200 | 1.9788        | 2.7243          | 0.7751                              | -                                    |
+| 4.8644 | 2300 | 1.1676        | 2.9885          | 0.7567                              | -                                    |
+| 5.0742 | 2400 | 1.0009        | 2.7374          | 0.7767                              | -                                    |
+| 5.2860 | 2500 | 2.1276        | 2.7822          | 0.7767                              | -                                    |
+| 5.4979 | 2600 | 1.8459        | 2.7822          | 0.7760                              | -                                    |
+| 5.7097 | 2700 | 1.7659        | 2.7322          | 0.7766                              | -                                    |
+| 5.9216 | 2800 | 0.5916        | 3.0191          | 0.7596                              | -                                    |
+| 6.1314 | 2900 | 1.3908        | 2.6973          | 0.7772                              | -                                    |
+| 6.3432 | 3000 | 1.9257        | 2.7585          | 0.7763                              | -                                    |
+| 6.5551 | 3100 | 1.6558        | 2.7350          | 0.7760                              | -                                    |
+| 6.7669 | 3200 | 1.5368        | 2.7903          | 0.7722                              | -                                    |
+| 6.9788 | 3300 | 0.1968        | 3.0849          | 0.7479                              | -                                    |
+| 7.1886 | 3400 | 1.8044        | 2.6626          | 0.7825                              | -                                    |
+| 7.4004 | 3500 | 1.7048        | 2.7380          | 0.7790                              | -                                    |
+| 7.6123 | 3600 | 1.5666        | 2.7250          | 0.7796                              | -                                    |
+| 7.8242 | 3700 | 1.0954        | 2.9620          | 0.7629                              | -                                    |
+| 8.0339 | 3800 | 0.487         | 2.8900          | 0.7641                              | -                                    |
+| 8.2458 | 3900 | 1.8398        | 2.7186          | 0.7796                              | -                                    |
+| 8.4576 | 4000 | 1.5659        | 2.7259          | 0.7778                              | -                                    |
+| 8.6695 | 4100 | 1.4825        | 2.7007          | 0.7760                              | -                                    |
+| 8.8814 | 4200 | 0.7019        | 2.9050          | 0.7675                              | -                                    |
+| 9.0911 | 4300 | 0.9278        | 2.7606          | 0.7731                              | -                                    |
+| 9.3030 | 4400 | 1.766         | 2.6978          | 0.7787                              | -                                    |
+| 9.5148 | 4500 | 1.4699        | 2.7114          | 0.7801                              | -                                    |
+| 9.7267 | 4600 | 1.4647        | 2.7096          | 0.7799                              | -                                    |
+| 9.9386 | 4700 | 0.3321        | 2.7418          | 0.7764                              | -                                    |
+| 9.9809 | 4720 | -             | -               | -                                   | 0.7741                               |
+
+
+### Framework Versions
+- Python: 3.10.14
+- Sentence Transformers: 3.3.1
+- Transformers: 4.46.3
+- PyTorch: 2.4.0
+- Accelerate: 0.27.2
+- Datasets: 3.1.0
+- Tokenizers: 0.20.3
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply},
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "microsoft/mpnet-base",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.46.3",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.3.1",
+    "transformers": "4.46.3",
+    "pytorch": "2.4.0"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b8aade3946a641859b60c68b877ff2dd0693f9a0184c3f64e2f98f87e526355d
+size 437967672

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,59 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}